Microsoft Word - Apostoaie Constantin Marius_eng.doc


 
 

Modeling and simulation of economic processes 
 

Bogdan Alexandru Brumar, Alma Mater University of Sibiu, Romania 
 
 
Abstract 
 
In general, any activity requires a longer action often characterized by a degree of uncertainty, 
insecurity, in terms of size of the objective pursued. Because of the complexity of real economic 
systems, the stochastic dependencies between different variables and parameters considered, not all 
systems can be adequately represented by a model that can be solved by analytical methods and 
covering all issues for management decision analysis-economic horizon real. Often in such cases, it is 
considered that the simulation technique is the only alternative available. Using simulation techniques 
to study real-world systems often requires a laborious work. Making a simulation experiment is a 
process that takes place in several stages. 
 
Keywords 
 
integration of economic process, ontology, transition system, optimization methods. 
 
JEL Codes: C 63 
 
 
General issues concerning technologies of economic simulation 
 
In this new millennium application integration concepts, techniques and technologies have 
become couples being able to sustain rapid economic change. To isolate the context of the 
complexity of integrating applications, such tactics and technology, this new phase will be 
referred to the integration of economic processes. 
Responding to change has rapidly become the number one problem of economic processes. 
Pressures for change occurring in all directions, and how the response of economic processes 
is quickly discernible results. Once the organization acquires and develops new applications 
to keep in touch with change, Enterprise Application Integration (EAI) is a major tool to 
integrate existing applications (Raţiu-Suciu, 1999). Moreover, in many cases the approach is 
oriented tactical and technology, by making simple bridges between one application and 
another. Although the immediate problem was solved, the base than the new wave of change. 
It appears as a constantly changing economic environment and support that the IT 
environment. Time restrictions, in particular, determines the reuse of existing systems and 
climate change is driven by the need for a strong architectural foundation to enable the 
prompt, try to define the field of applications in this context becomes more complicated. 
Implementation of specific components of economic processes distributed systems customers 
and partners throughout the value chain. Relations must be established and separated very 
quickly. 
Economic integration is not a phenomenon purely technical substance, it needs a hierarchy of 
economic processes, applications and technical levels, with shared concepts and interfaces 
between them to be accepted by all participants. It recommends a service oriented approach 
to establish mutual agreement between the economic and IT.  
Ultimately, success depends on the adequacy of the fundamental economic and IT goals. IT 
strategy needs to be aware of all the factors affecting the decisions of economic integration 
processes such as configuration of economic processes, their borders and place where change 

Studies and Scientific Researches - Economic Edition, no. 15, 2010

322



is most likely to occur. Understanding economic goals, such as merger and acquisition 
strategies or cost and increase efficiency, appears as a fundamental key (Popescu-
Bogdăneşti, 1999). 
In general, the scientific study of a system or phenomenon can be done by doing real or 
artificial. Economic, real experimentation is rare because it involves high costs and risks, 
while artificial testing, although sometimes requires great intellectual and financial effort, 
will ensure that real situations with sometimes catastrophic implications. For such problems, 
disciplines such as: systems theory, decision theory, operational research, economic 
cybernetics, etc. use appropriate mathematical models.  
Often in such cases, it is considered that the simulation technique is the only alternative 
available. 
Using simulation techniques to study real-world systems often requires a laborious work. 
Making a simulation experiment is a process that takes place in stages. 
The main stages of the simulation are: 

 analysis and synthesis systems and processes;; 
 conception and model design; 
 scheduling model simulation; 
 validation of the simulation model; 
 simulation itself; 
 analysis and results implementation. 

Economic modeling provides managers vigorously its side (“science of driving”), multiple 
ways of reconciling the resources (material, human, financial) available to the objectives 
formulated for a certain period of time, giving him the opportunity to find and to decide 
“better” and “faster” without distorting reality (Stoica M., 1994). 
 
The process of transition from the real system to model simulation 
 
Obtaining information about the system “before” it can be done in concrete is possible with 
simulation technology. Simulation is a technique for making computer numerical 
experiments, involving construction of mathematical and logical models that describe the 
behavior of a real system (or of its components) over a period of time. 
Simulation needs to generate inputs and taking into account the internal states of the system 
by appropriate algorithms to determine the outputs and to describe over time the internal 
state of the system. 
While not providing exact solutions (but sub-optimal), simulation is an effective research 
technique for complex economic issues at company level, can not be studied analytically 
(economic-mathematical methods of optimization). 
Using simulation to obtain several versions of a decision of the manager will choose the best 
corresponding current circumstances at some point. 
Consequences of real experience, without experience "simulated", can sometimes be harmful 
to business management. If an existing system (company, enterprise), its behavior can be 
provided by a simulation model that highlights the effect of the change of parameters 
describing the system. The simulation work involved three major elements, namely the real 
system, model, computer and two relations: relations modeling and simulation relations.  
“Real system” is human perception system. The “real model” means the replacement of real 
system and corresponds, in principle, with the original system. The “abstract model” made 
the transition from “real system” to “real model”. It replicates the real system by 
decomposing basic system parts and establishes links between them. The validation is done 
by determining the consistency of data from the real and model provided. Because in real 
systems trends are influenced by random causes whose effect must be demonstrated in the 
simulation model, one of the important mathematical problems of numerical simulation is 
generated using computer statistical selection of the various random variables and statistical 
processes (Văduva I., 1983).  

Studies and Scientific Researches - Economic Edition, no. 15, 2010

323



Another important issue related to building simulation models is the evolution of states 
cronometrării simulated system. Simulation algorithm must contain a variable called 
“simulation time” which is subject to a finite number increases during the simulation. Today 
attention is given to simulation of economic activity. Known methods for generating random 
variables of different types of mathematical models for simulation of economic processes 
and to build practical simulation models are now software packages and specialized 
computer languages.  
Simulation is the technique of making computer experiments, involving the use of 
mathematical and logical models that describe the behavior of a real system (or some of its 
components) over a period of time. Known variants of simulation: simulation type game and 
Monte Carlo analysis. Simulation type game refers to those situations that are characterized 
by a “conflict” between partners or between humans (which must take certain decisions) and 
nature (which offers more human variants that he will choose the one convenient). 
Simulation type game has broad applications in problems of organization and management 
of economic activity. Analysis of Monte Carlo simulation is a technique related to problems 
with more than random. Monte Carlo analysis was shaped as a field of mathematics itself, in 
connection with the settlement of purely deterministic problem that can not be solved by 
deterministic methods (Văduva I., 1983). 

 
Simulation models are classified into: deterministic, stochastic, static and dynamic. 
 
Deterministic models are those for which all variables are nealeatoare, operational 
characteristics of such models will be assumed by some form equations instead of probability 
density. There are plenty of ways to solve for them and because of this simulation technique 
is not always required to solve them. 
Stochastic models are those in which at least one variable input is random and therefore one 
of the operational characteristics is given by a density distribution. These models are more 
complicated than the deterministic, analytical techniques appropriate for their resolution is 
too low, and thus they will occupy an important place among the simulation models. 
Static models are those which do not explicitly take into account the variable time. Most of 
them are deterministic and their solutions can be obtained analytically. 
Dynamic models are those which take account of variation and interaction while the variables 
considered and are therefore appropriate simulation of economic systems. Simulation models 
are usually stochastic and dynamic models. 
 
Decision support systems 
 
Decision support system (DSS) is essentially a coherent set of tools used in making 
decisions. The idea of multidimensionality, ie taking into account multiple criteria 
simultaneously leading inevitably to issues studied in class classification problems. To solve 
such a class of problems (identified in the literature by the so-called problem type) is called 
becoming more methods, techniques and tools (so-called procedures) Mathematics, Statistics 
and Informatics. The following are some methods of calculation useful in solving problems 
most common type in the economic activity that incorporates: statistical analysis of 
experimental data, optimization in modeling complex decision-making, planning and control 
complex network projects, cost analysis -benefit analysis, simulation of complex decision 
(business / management games) etc.. Experience shows that industrialized countries using 
economic and mathematical models and software performance coefficients results in 
significant increases in load capacity, significant reduction in stocks of materials, cuts 
transportation costs, saving material resources, financial, human and simplification collection 
operations and data processing. 
The objectives pursued by the use of SSD's in the disciplines of the curriculum are:  

Studies and Scientific Researches - Economic Edition, no. 15, 2010

324



 economic integration Mathematical Models and computer facilities provided by the 
economic processes in research and research processes;  

 training of users and implementers of SSD and reusable software applicable 
economic and technical management of production processes;  

 training mathematical thinking (in essence triad Model - Algorithm - Product 
Program) that will allow the study of real problems and realistic solutions in terms 
of efficiency;  

Usually models are classified into: verbal models, graphical models, mathematical models 
(Raţiu-Suciu C., 2002). In the literature are distinguished different classes of models: 
physical models, analogical, symbolic, mathematical, economic and mathematical, etc. An 
economic-mathematical model provides essential information to substantiate the decision 
cycle: FORECAST-SIMULATION-OPTIMIZATION. 
 
Modeling complex process of decision 
 
The complexity of economic processes requires detailed study of the issue decisions that take 
into account multiple criteria simultaneously. Decision theory emerges as a subject of study 
around the years 1950 and from the outset and aims to become an effective logical decisions. 
Depth studies in this area and complexity that require calculations made in a timely manner 
had a great impact on what we call today's decision support systems. 
Decision Theory considers that a decision process is characterized by the following 
elements: 

• decision criteria (points of view from which examines the issue); 
• the objective to be aimed; 
• decision-maker, that person or group of persons who seek to make the decision to 

achieve the best conditions of the objective or objectives; 
• set containing all possible alternatives for action to achieve the objectives 

considered; 
• crowd possible states, each state representing the complex of conditions that cause a 

certain impact for a particular alternative and a clear objective; 
• alternatives including possible consequences set exactly how many alternatives 

there are consequences (single state - a condition of certainty), or several possible 
consequences of each alternative (more possible states - conditions of risk or 
uncertainty); 

• utility they expect to achieve a decision-maker from certain consequences. 
 
Current trends in the enterprise computerization 
 
The term "Information Society" is used to describe current changes (mutations) in all fields 
(economy, politics, culture, education, health, legal, trade, etc.). Following widespread use of 
IT & C. Information society is to support computer networks providing access to digital data 
stored in text, sound and images (multimedia). Intemetul, originally designed as a global 
information network, has become the global communications platform that provides storage 
of huge volumes of data and information and quick access to prices ever lower. 
The transition to information society divides traditional organizations and businesses in 
modern organizations using IT & C and systems for businesses. Great software producers 
(Microsoft, IBM, etc..). Develops technologies that allow building applications that operate 
in rural Internet / Intranet. For this purpose a company's computer system includes the 
following modules: 

 Entreprise Relationship Planning - ERP, which is a system that integrates key 
business processes that occur in business, namely: Finance, Resources 
Management, Purchasing, planning and monitoring production, sales. 

Studies and Scientific Researches - Economic Edition, no. 15, 2010

325



 Customer Relationship Management - CRM - (the entire complex of interactions 
between the enterprise and its customers). CRM facilitates the provision of services 
via Internet, telephone, ATM / Kiosk etc.. 

 E-commerce (e-Business) - Business Online in Internet-access website of ERP. E-
business can take one of the forms: Business-to-Business (B2B), Business-to-
Consumer (B2C), Supply Chain Management (SCM). 

 Business Intelligence (BI) - applications for collecting, storing and processing data 
for decision making. BI applications include activities such as: 

• Decision Suport Systems - SSD 
• Online Analytical Processing - OLAP 
• Data Warehouse 
• Data Mining. 

CRM systems and e-Business ERP components interact to perform desired functions. in this 
respect, e-Business (B2C or B2B) calling ERP components for processing or processed by 
the ERP data are regularly archived and processed data warehouse using OLAP tools, etc.. 
The test results are used by components BI to make decisions and to develop business plans.  
The enterprise is a system (a living organism) to evolve after its development law, in any 
business identified the following subsystems: Research - Development, Commercial, 
Production, Personnel and Finance - Accounting. Cropping system into subsystems (areas of 
study) is made as functions of the enterprise (Popescu-Bogdăneşti, 1999). The main task 
undertaken by the management company is to coordinate transformations in the sub set to 
achieve performance targets proposed in the system, provided it is known that any 
transformation entails changes. Within each subsystem, within any enterprise, activity base 
has a specific frequency. For operational management activities necessary to ensure that the 
basic cycle activity can be collect and process data for the preparation of the decision 
together with the development, communication and tracking to implementing decisions 
relating to business cycles analyzed. In any company who has mastered the information 
flows ensured success. 
 
Software development – concepts 
 
The development of computers, have built software for them, software that are increasingly 
complex. Therefore, development cost, maintenance and use of a software system for solving 
problems has become quite expensive. Current methodology for dealing with the computer is 
encoded in a program in computer memory. Implementation of this program is described by 
repetitive structure: 

while((PC). opCode≠ Halt) Execute (PC); PC:=Next(PC) 
 

Here, PC is an index which is the current instruction address value of the program, Execute 
() performs the operation encoded in instruction and function Next () determines the address 
of next instruction program. Computer programs are based on the repetitive structure when 
used for troubleshooting.  
The current trend is that a soft approach to human logic to solve problems. This approach 
must be accompanied by a learning methodology that uses previous experience to develop 
intuitive software system that solves a class of problems facing the areas. This can be 
achieved by a methodology of solving the problem where users manipulate computer 
processes instead of data representation and machine operations, following the logic of the 
problem instead to follow the logic machine running the program. Handling processes 
computer users will have a higher level of abstraction, which are solutions to sub-problems 
of the problem data. It follows as a consequence that computer users must be provided 
maintenance populated specification mechanisms, tools and independent software 
components.  

Studies and Scientific Researches - Economic Edition, no. 15, 2010

326



Specification mechanisms allow the user to adapt the issue without a formal framework, the 
tools are used to generate the correct software components correct specifications, 
independent software components are universal algorithms outside the scope of the class of 
problems specified and they operate on structures data generated by the tools and functions 
in the specification.  
Programming languages be used to express the software system architecture that solves a 
class of problems in terms of independent components compatible with PSE (Problem 
Solving Environment). Raţiu-Suciu (1999) proposed a new application development 
methodology based on separation of the functionality of the software system architecture 
developed by this system. The system architecture is described by the language of 
description of architectures (Architecture Description Language-ADL), while the 
functionality of the system still is described by any high level programming language or low. 
ADL's used by the computer user is problem-oriented field and is built to the highest level of 
vocabulary, which preprezintă problem domain ontology. Each ontological term used by the 
ADL is associated with one or more independent software components and these 
components implement functionality.  
Current research led to the development of architecture description languages, a language 
such as that presented by Garlan which was based on the following ontology: 

 One area of application is characterized by a lot of software architecture called styles. A 
style specifies a family of related systems and defines a vocabulary of designs and types 
of elements which constitute the theme of values. 

 Types of elements determined to define a style are: 
 Components are computational units of the system. The components are equipped 

with interfaces called ports. 
 Connectors represent interactions between components. Connectors are provided 

with interfaces called roles. 
 Systems, wich are graphs whose nodes are components and edges are connectors. 

System topology is defined by attaching explicit roles of ports and connectors to attach 
ports roles components connectors. 

 An element can have one or more descriptions in terms of other elements, descriptions 
called representations. It contains a hierarchical description of architectures in terms of 
other architectures. 

 An element can have one or more properties that represent semantic information. 
 An element can have one or more constraints as system architecture can be developed 

while maintaining baseline. 
A problem solver uses a domain to express problem-oriented ADL system architecture that 
solves the problem. An interpreter examines the architectural expression, localized functions 
that implement various components of the architecture, creating processes developed by 
these components and compose these processes in a process represented by architectural 
expression that solves the problem (Văduva I., 1983). 
An environmental problem solving (problem solving environment - PSE) refers to a range of 
problem identified by:  
(1) a collection of specification mechanisms;  
(2) instruments operating mechanisms for specifying data and transforming them into 
corresponding operations parts;  
(3) components to solve classes of problems through universal algorithms in terms of data 
and transactions generated by the instruments.  
There is a dynamic relationship between the specifications, tools and components, where the 
tools can be used as components and parts can be used as instruments. The effect of this 
approach is a hierarchical methodology where problem-solving algorithms are decomposed 
in terms of more primitive algorithms above are implemented (Raţiu-Suciu C., 2002).  

Studies and Scientific Researches - Economic Edition, no. 15, 2010

327



The first step in the development of a language is to establish the ontology language and the 
second step in the development of language is the combination of ontology nodes with 
computational significance. But these are matters which are subject of another study of mine. 
 
Conclusions 
 
In general, the scientific study of a system or phenomenon can be done by doing real or 
artificial. In the economic field, real experimentation is rare because it involves high costs 
and risks, while the artificial testing, although sometimes requires great intellectual and 
financial effort, will ensure that real situations with sometimes catastrophic implications. As 
long as the key instrument for achieving economic integration is the implementation 
representation of flows, classical design principles remain valid. Sequencing activities to 
identify which services should be listed first (not necessarily the same) and should be used 
consistently (Raţiu-Suciu, 2002). For all these the programmers have created models and 
architectures, which in the early new millennium have resulted in managers dedicated 
software. It is about the formal languages, based on which they were created economic 
modeling and simulation. 
 
 
Bibliography 
 
1. Popescu-Bogdăneşti, C. (1999), Sistemul informaţional al firmei în mediu concurenţial, Ed. 

Tribuna Economică, Bucureşti. 
2. Raţiu-Suciu, C. (2002), Modelarea şi simularea proceselor economice. Teorie şi practică. Ediţia a 

II-a, Ed. Economică, Bucureşti. 
3. Raţiu-Suciu, C., Luban, F., Hîncu, D., Ene, N. (1999), Modelarea şi simularea proceselor 

economice. Lucrări practice. Studii de caz. Teste de autoevaluare. Ediţia a II-a. Ed. Didactică şi 
Pedagogică, Bucureşti. 

4. Stoica, M., Raţiu-Suciu, C., Mincu, C. (1994), Modelarea microeconomică; Ştiinţă & Artă, Ed. 
AISTEDA-Omega Press, Bucureşti. 

5. Văduva, I., Stoica, M., Odăgescu, I. (1983), Simularea proceselor economice, Ed. Tehnică, 
Bucureşti. 

6. www.boc-eu.com 

Studies and Scientific Researches - Economic Edition, no. 15, 2010

328